1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
16/9/2023 Comida 79913 Tami Supermercado
16/9/2023 Enceres 14400 Tami Incoludido
16/9/2023 Comida 18580 Andrés Johnny Rockets
16/9/2023 Comida 38151 Andrés Frutos secos
16/9/2023 Diosi 21081 Andrés antiparasitario
17/9/2023 Diosi 8000 Andrés arena
18/9/2023 Comida 10000 Andrés empanadas (3) menos helado q me comí
20/9/2023 VTR 21990 Andrés NA
16/9/2023 Comida 27980 Tami Cajas Soul Bar
23/9/2023 Comida 57639 Tami Supermercado
24/9/2023 Diosi 8000 Andrés arena diosi 10kg
30/9/2023 Electricidad 44407 Andrés NA
30/9/2023 Comida 51726 Tami Supermercado
6/10/2023 Comida 44298 Tami Supermercado
14/10/2023 Comida 86673 Tami Supermercado
10/10/2023 Diosi 6880 Tami Omega aceite petsu
17/10/2023 Diosi 55990 Tami Comida Diosi
17/10/2023 Diosi 50000 Tami Veterinaria
20/10/2023 Comida 41970 Tami Barritas Wild Soul
21/10/2023 Diosi 56170 Andrés n y d + 2 tarrito
21/10/2023 Comida 50000 Andrés la providencia
21/10/2023 Comida 76052 Tami Supermercado
21/10/2023 Diosi 55990 Andrés Pelet SuperZoo
26/10/2023 Comida 17493 Tami Chicken love u
30/10/2023 Comida 61933 Tami Supermercado
31/10/2023 Diosi 20000 Tami Veterinaria
1/11/2023 Electricidad 44414 Andrés NA
5/11/2023 Comida 47648 Tami Supermercado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.3239e+08   2    8.0676  3e-04 ***
## lag_depvar    8.8718e+10   1 1719.7240 <2e-16 ***
## Residuals     3.2604e+10 632                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838  1048.456 13409.22 0.0169606
## 2-0 29107.287 23487.138 34727.44 0.0000000
## 2-1 21878.449 18559.052 25197.85 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
## 31   25617.86             0   28331.57
## 32   27223.29             0   25617.86
## 33   31622.57             0   27223.29
## 34   32021.43             0   31622.57
## 35   33634.57             0   32021.43
## 36   30784.86             0   33634.57
## 37   34770.57             0   30784.86
## 38   38443.00             1   34770.57
## 39   35073.00             1   38443.00
## 40   31422.29             1   35073.00
## 41   30103.29             1   31422.29
## 42   19319.29             1   30103.29
## 43   27926.29             1   19319.29
## 44   30715.43             1   27926.29
## 45   31962.29             1   30715.43
## 46   39790.14             1   31962.29
## 47   39211.57             1   39790.14
## 48   44548.57             1   39211.57
## 49   49398.00             1   44548.57
## 50   41039.00             1   49398.00
## 51   34821.29             1   41039.00
## 52   29123.57             1   34821.29
## 53   21275.71             1   29123.57
## 54   28476.14             1   21275.71
## 55   24561.86             1   28476.14
## 56   20323.57             1   24561.86
## 57   25370.00             1   20323.57
## 58   26811.86             1   25370.00
## 59   27151.86             1   26811.86
## 60   27623.29             1   27151.86
## 61   22896.57             1   27623.29
## 62   41889.29             1   22896.57
## 63   44000.14             1   41889.29
## 64   38558.00             1   44000.14
## 65   43373.86             1   38558.00
## 66   49001.00             1   43373.86
## 67   61213.29             1   49001.00
## 68   58939.57             1   61213.29
## 69   42046.86             1   58939.57
## 70   39191.71             1   42046.86
## 71   42646.43             1   39191.71
## 72   36121.57             1   42646.43
## 73   30915.57             1   36121.57
## 74   20273.43             1   30915.57
## 75   23938.29             1   20273.43
## 76   19274.29             1   23938.29
## 77   21662.29             1   19274.29
## 78   15819.00             1   21662.29
## 79   18126.14             1   15819.00
## 80   17240.71             1   18126.14
## 81   16127.71             1   17240.71
## 82   13917.14             1   16127.71
## 83   15379.86             1   13917.14
## 84   19510.14             1   15379.86
## 85   24567.29             1   19510.14
## 86   25700.43             1   24567.29
## 87   25729.00             1   25700.43
## 88   26435.00             1   25729.00
## 89   31157.14             1   26435.00
## 90   29818.43             1   31157.14
## 91   30962.43             1   29818.43
## 92   28746.71             1   30962.43
## 93   27830.71             1   28746.71
## 94   28252.14             1   27830.71
## 95   28717.57             1   28252.14
## 96   21365.43             1   28717.57
## 97   24816.86             1   21365.43
## 98   16838.57             1   24816.86
## 99   15529.14             1   16838.57
## 100  13286.29             1   15529.14
## 101  13629.43             1   13286.29
## 102  14404.86             1   13629.43
## 103  19524.86             1   14404.86
## 104  18475.71             1   19524.86
## 105  22495.00             1   18475.71
## 106  22254.57             1   22495.00
## 107  24173.29             1   22254.57
## 108  27466.43             1   24173.29
## 109  24602.43             1   27466.43
## 110  20531.14             1   24602.43
## 111  20846.43             1   20531.14
## 112  23875.71             1   20846.43
## 113  36312.71             1   23875.71
## 114  34244.00             1   36312.71
## 115  36347.43             1   34244.00
## 116  39779.71             1   36347.43
## 117  42018.71             1   39779.71
## 118  39372.57             1   42018.71
## 119  33444.00             1   39372.57
## 120  29255.86             1   33444.00
## 121  31640.14             1   29255.86
## 122  29671.14             1   31640.14
## 123  31023.71             1   29671.14
## 124  39723.43             1   31023.71
## 125  39314.14             1   39723.43
## 126  38239.86             1   39314.14
## 127  34649.43             1   38239.86
## 128  36688.43             1   34649.43
## 129  42867.57             1   36688.43
## 130  42226.86             1   42867.57
## 131  32155.14             1   42226.86
## 132  33603.00             1   32155.14
## 133  37254.43             1   33603.00
## 134  33145.57             1   37254.43
## 135  31299.43             1   33145.57
## 136  30252.00             1   31299.43
## 137  26310.71             1   30252.00
## 138  27929.86             1   26310.71
## 139  27666.14             1   27929.86
## 140  25017.57             1   27666.14
## 141  27335.00             1   25017.57
## 142  25760.71             1   27335.00
## 143  18436.86             1   25760.71
## 144  21906.00             1   18436.86
## 145  19418.14             1   21906.00
## 146  22826.14             1   19418.14
## 147  23444.29             1   22826.14
## 148  25264.86             1   23444.29
## 149  25473.29             1   25264.86
## 150  27366.86             1   25473.29
## 151  28855.86             1   27366.86
## 152  32326.86             1   28855.86
## 153  27141.43             1   32326.86
## 154  26297.71             1   27141.43
## 155  23499.14             1   26297.71
## 156  30246.29             1   23499.14
## 157  39931.86             1   30246.29
## 158  38020.43             2   39931.86
## 159  35004.00             2   38020.43
## 160  40750.86             2   35004.00
## 161  42363.29             2   40750.86
## 162  46273.57             2   42363.29
## 163  41083.29             2   46273.57
## 164  35711.29             2   41083.29
## 165  41921.71             2   35711.29
## 166  60583.29             2   41921.71
## 167  63115.57             2   60583.29
## 168  61300.14             2   63115.57
## 169  57666.43             2   61300.14
## 170  55834.00             2   57666.43
## 171  58927.71             2   55834.00
## 172  57810.57             2   58927.71
## 173  48987.14             2   57810.57
## 174  52219.29             2   48987.14
## 175  56503.57             2   52219.29
## 176  56545.00             2   56503.57
## 177  64705.57             2   56545.00
## 178  53833.29             2   64705.57
## 179  50114.00             2   53833.29
## 180  39592.43             2   50114.00
## 181  29907.29             2   39592.43
## 182  33923.29             2   29907.29
## 183  45489.00             2   33923.29
## 184  44866.29             2   45489.00
## 185  51680.57             2   44866.29
## 186  58257.00             2   51680.57
## 187  70600.57             2   58257.00
## 188  76648.00             2   70600.57
## 189  69430.14             2   76648.00
## 190  69651.57             2   69430.14
## 191  77745.14             2   69651.57
## 192  72795.86             2   77745.14
## 193  67670.71             2   72795.86
## 194  55357.86             2   67670.71
## 195  48524.00             2   55357.86
## 196  50154.43             2   48524.00
## 197  45111.57             2   50154.43
## 198  36147.00             2   45111.57
## 199  43501.57             2   36147.00
## 200  41472.43             2   43501.57
## 201  41058.00             2   41472.43
## 202  41605.57             2   41058.00
## 203  49382.86             2   41605.57
## 204  59558.57             2   49382.86
## 205  59134.57             2   59558.57
## 206  61109.00             2   59134.57
## 207  63004.43             2   61109.00
## 208  67344.29             2   63004.43
## 209  78180.86             2   67344.29
## 210  69117.86             2   78180.86
## 211  55597.57             2   69117.86
## 212  49426.14             2   55597.57
## 213  39119.43             2   49426.14
## 214  35636.86             2   39119.43
## 215  39201.14             2   35636.86
## 216  27777.00             2   39201.14
## 217  47207.00             2   27777.00
## 218  55587.29             2   47207.00
## 219  56619.71             2   55587.29
## 220  82679.86             2   56619.71
## 221  91259.57             2   82679.86
## 222  93552.71             2   91259.57
## 223 102242.71             2   93552.71
## 224  91884.00             2  102242.71
## 225  85013.86             2   91884.00
## 226  84535.29             2   85013.86
## 227  80700.43             2   84535.29
## 228  79740.57             2   80700.43
## 229  85163.14             2   79740.57
## 230  86724.86             2   85163.14
## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   480 51341.55 15145.972
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   1918.809183   3997.487990   -495.468533   2475.004848  -2885.386118 
##             7             8             9            10            11 
##    549.366996  -5610.826458  -1238.529753  -4022.141185   -527.297072 
##            12            13            14            15            16 
##  -5033.433870  -1768.749892  -1058.049425    232.545988  -3353.718821 
##            17            18            19            20            21 
##   -522.438634  -2253.847332   6467.837535  -1523.518748  -1221.020499 
##            22            23            24            25            26 
##   1452.436350  -1171.873729    235.848717   1709.310465  -7050.673083 
##            27            28            29            30            31 
##    877.211603   8156.810932    540.031617    109.085036  -2283.993528 
##            32            33            34            35            36 
##   1645.279441   4669.782920   1301.384418   2572.972550  -1658.129972 
##            37            38            39            40            41 
##   4767.890064   4398.103007  -2116.720531  -2881.590989  -1074.362121 
##            42            43            44            45            46 
## -10728.858261   7112.841986   2531.522582   1389.943316   8150.074943 
##            47            48            49            50            51 
##    868.247367   6700.697452   6979.874241  -5531.853803  -4591.476530 
##            52            53            54            55            56 
##  -4964.753905  -7933.467546   5987.343843  -4092.909927  -4979.261734 
##            57            58            59            60            61 
##   3696.552792    816.984195    -77.726350    102.548838  -5027.865516 
##            62            63            64            65            66 
##  18012.492462   3859.252488  -3390.488012   6085.658262   7588.821413 
##            67            68            69            70            71 
##  14982.396303   2250.892893 -12694.763726  -1084.109773   4815.558931 
##            72            73            74            75            76 
##  -4667.685718  -4286.232095 -10470.302856   2307.777513  -5494.562357 
##            77            78            79            80            81 
##    887.376993  -7000.833071    310.110976  -2551.001267  -2905.779137 
##            82            83            84            85            86 
##  -4163.251403   -807.550793   2070.163707   3590.404625    392.946741 
##            87            88            89            90            91 
##   -548.829996    132.703333   4250.274753  -1132.168558   1158.217305 
##            92            93            94            95            96 
##  -2037.142482  -1055.752157    150.077882    254.623058  -7496.081867 
##            97            98            99           100           101 
##   2251.232782  -8682.626772  -3159.982174  -4281.531791  -2017.755272 
##           102           103           104           105           106 
##  -1536.171418   2919.803135  -2513.767139   2403.934729  -1278.342765 
##           107           108           109           110           111 
##    846.258556   2496.342132  -3187.686351  -4806.432978  -1004.769001 
##           112           113           114           115           116 
##   1754.527001  11597.448223  -1121.485552   2753.452323   4384.501729 
##           117           118           119           120           121 
##   3684.320559   -879.152960  -4541.743667  -3653.052322   2317.680340 
##           122           123           124           125           126 
##  -1693.063343   1345.628707   8887.090795   1027.948472    304.147818 
##           127           128           129           130           131 
##  -2366.333930   2747.270263   7180.349155   1248.227963  -8274.821228 
##           132           133           134           135           136 
##   1797.782057   4209.362083  -3026.335595  -1353.926516   -820.441150 
##           137           138           139           140           141 
##  -3864.778711   1129.419057   -520.821466  -2943.565523   1641.923437 
##           142           143           144           145           146 
##  -1916.853950  -7892.597529   1848.209402  -3610.390916   1928.044448 
##           147           148           149           150           151 
##   -372.197194    919.037810   -431.549885   1283.537180   1151.008569 
##           152           153           154           155           156 
##   3346.928017  -4810.834063  -1214.092255  -3290.162894   5853.490374 
##           157           158           159           160           161 
##   9761.257480  -3599.590160  -4979.198452   3350.727467     41.929863 
##           162           163           164           165           166 
##   2571.439007  -5967.355280  -6894.739853   3915.912173  17259.285691 
##           167           168           169           170           171 
##   3811.043003   -172.866606  -2251.968626   -972.725998   3690.158223 
##           172           173           174           175           176 
##    -76.235754  -7943.017495   2844.922655   4361.416229    734.067506 
##           177           178           179           180           181 
##   8859.162262  -9001.293982  -3410.277436 -10746.899986 -11422.068968 
##           182           183           184           185           186 
##    887.643119   9014.322149  -1512.500499   5835.036307   6576.163880 
##           187           188           189           190           191 
##  13288.119344   8765.334440  -3631.138256   2771.183040  10675.137769 
##           192           193           194           195           196 
##  -1204.944131  -2091.847930 -10015.873655  -6305.818993   1176.670247 
##           197           198           199           200           201 
##  -5262.377468  -9908.581491   5122.652585  -3204.456017  -1881.261624 
##           202           203           204           205           206 
##   -978.801135   6329.580832   9845.344978    707.540153   3045.054120 
##           207           208           209           210           211 
##   3249.713402   5966.451601  13086.658060  -5256.060862 -11015.396248 
##           212           213           214           215           216 
##  -5608.951504 -10630.864885  -5287.451803   1259.076422 -13217.283625 
##           217           218           219           220           221 
##  15995.592383   7737.318583   1593.427926  26769.467632  13033.009098 
##           222           223           224           225           226 
##   7979.055364  14705.360361  -3094.891865  -1094.520870   4310.041041 
##           227           228           229           230           231 
##    885.000635   3209.060054   9453.589290   6371.774115  -1335.431257 
##           232           233           234           235           236 
##  -1360.566466   9803.875813 -11022.763564  -7050.073324  -8458.023035 
##           237           238           239           240           241 
## -10169.950256   2851.502247   1206.610591  -8400.782627  -9215.871052 
##           242           243           244           245           246 
##   8750.395437  -7914.853241   2230.000105 -10483.741174  -4381.725114 
##           247           248           249           250           251 
##   1071.157484    720.584966 -12545.288460   3241.684317   1772.390082 
##           252           253           254           255           256 
##   3992.565206   2012.031287  -1233.490979  11055.630725  20987.419569 
##           257           258           259           260           261 
##   3623.835014  -3852.295527   4401.302643  -1377.426463   3982.623076 
##           262           263           264           265           266 
##  -4580.690119 -10739.147167  -4773.023129   -639.071177  -5301.060831 
##           267           268           269           270           271 
##   8593.658537  -4303.000655   4097.219780  -2123.672916   4378.582067 
##           272           273           274           275           276 
##    730.350527   7328.461525  -1278.351008  12113.579532  -4322.907329 
##           277           278           279           280           281 
##   1872.831261   -222.912917   7969.435027  -4836.958413  -2624.603932 
##           282           283           284           285           286 
## -11215.991641  -2809.773603  18487.985857   7926.257870   2974.014228 
##           287           288           289           290           291 
##   -383.504365   1103.225450   6577.849270   7131.984789 -18454.800486 
##           292           293           294           295           296 
## -11156.795203  -8309.884161   9376.036368   2969.333969  -1222.805289 
##           297           298           299           300           301 
##  27343.904384  10430.914466   5367.116850   9992.633405   3409.049773 
##           302           303           304           305           306 
##   -514.068258   8326.184338 -23806.236244  -3480.350032   -181.814166 
##           307           308           309           310           311 
##  -6976.478208  -4083.599071   2776.754207  -9281.213350  -3443.215910 
##           312           313           314           315           316 
##  -8416.093791   1247.357170  -3399.666989   1788.839815  -4274.324542 
##           317           318           319           320           321 
##  27219.910034   -528.994340   3445.821784  11009.979130   5902.491598 
##           322           323           324           325           326 
##  32731.968816   5895.823017 -20181.843378   2135.048487   1433.057118 
##           327           328           329           330           331 
##  -6169.811797  -1574.574828 -33154.710535    525.425555  -2586.259874 
##           332           333           334           335           336 
##   -361.403662  -3390.152146   3857.726917   -564.256336  -7058.933231 
##           337           338           339           340           341 
##  -3299.520148  -2384.431683  -7867.171466   3588.838367  -1532.419735 
##           342           343           344           345           346 
##  -1886.886703  -1137.667651     48.225440    383.227092  -1687.125594 
##           347           348           349           350           351 
##  -9520.253648 -13401.443702   1963.041595  -4580.108445  -3932.857635 
##           352           353           354           355           356 
##  -6259.563994   1433.145929   1144.215763   2573.653419  -3875.108854 
##           357           358           359           360           361 
##   -657.092931    555.462524   6924.872451    305.683591     -3.194025 
##           362           363           364           365           366 
##   2618.829560  -2676.643329   -845.526050  -8720.448861  -4719.610294 
##           367           368           369           370           371 
##  -6348.064728  -5143.239563  -7478.563000   4728.804805    202.536915 
##           372           373           374           375           376 
##   6987.655865  -7643.466458  -2366.766256  -3499.258068  -2600.245966 
##           377           378           379           380           381 
## -12596.515602   1619.553550 -10842.084079   5378.457611   9149.950708 
##           382           383           384           385           386 
##   3102.075736  -2369.681958   1602.922332   6771.053426  11532.041145 
##           387           388           389           390           391 
##  -5537.945685  -5215.582641   -103.361142   8610.448713   1975.408666 
##           392           393           394           395           396 
##  11385.914006  -9583.401020   2890.015094    850.426069    690.794000 
##           397           398           399           400           401 
##   -535.378104   -469.800469 -14414.049975   8395.069008  -1165.751480 
##           402           403           404           405           406 
##  -1369.289884   6972.119623  -7840.234975  -1318.165152  -2558.456465 
##           407           408           409           410           411 
##  -5866.659946  -2967.603983  -4037.769024  -8900.624176   5901.495577 
##           412           413           414           415           416 
##   1533.728923  -7433.988235  -7834.067484  14005.687626   3828.283369 
##           417           418           419           420           421 
##   4557.335581  -7915.717602  -4742.144339  -2653.551007   2750.120167 
##           422           423           424           425           426 
## -14023.311863  -2980.409587  -9287.374779   2739.958708   6793.444151 
##           427           428           429           430           431 
##   6519.772420  -3939.555879  -4124.389622  -4770.779130  -1884.485281 
##           432           433           434           435           436 
##  -5807.017453  -6774.872814  -6155.695738  -1639.891226  -1067.818633 
##           437           438           439           440           441 
##  -5165.084658   2361.739963   4695.350813  -5105.899371  -2258.745255 
##           442           443           444           445           446 
##   1471.186414  -3896.068758   2743.401121  -6608.881930 -12215.469656 
##           447           448           449           450           451 
##  -4755.084960   9385.858282  -2112.149385   4669.473906  -5866.228661 
##           452           453           454           455           456 
##  -1187.945548    324.419251   2992.369740 -12244.532026   3233.391911 
##           457           458           459           460           461 
##  -6759.910808   6391.787294   3001.371976   2550.592719  -3762.407721 
##           462           463           464           465           466 
##   2123.419113     59.404340   1861.835973   -425.973844   3436.540240 
##           467           468           469           470           471 
##  -2507.875016   5893.853528  -6773.955987  -2901.486856  -2177.986217 
##           472           473           474           475           476 
##  -4656.377884   2952.861682   7814.627719  -5881.885534   1531.379211 
##           477           478           479           480           481 
##  -6104.063027  -2851.136438   1981.116691 -12914.666547  -9907.842389 
##           482           483           484           485           486 
##  -1456.052976   -201.463766  -1139.373266  -1494.319230  -9721.179392 
##           487           488           489           490           491 
##  10857.499770   6198.334430   7488.444360  -5261.757501   5455.587196 
##           492           493           494           495           496 
##   9456.616385   6336.403819 -13133.738107 -10446.860372  -3471.080458 
##           497           498           499           500           501 
##  -1164.910460   -576.039561  -7663.204456    487.891183   4205.297638 
##           502           503           504           505           506 
##   5512.373129    754.964081    185.825724  -7132.561628    575.602762 
##           507           508           509           510           511 
##  -5020.441368   1801.174165  -1282.782917  -8149.194718   -693.752187 
##           512           513           514           515           516 
##  -2746.867425   -672.529667   1265.882528  -9518.773343  -7903.917666 
##           517           518           519           520           521 
##  24072.545079   9995.211118   6176.956225  -4983.679483   3040.389866 
##           522           523           524           525           526 
##  17279.977927  11948.525123 -23567.602333  -4883.486082  -3638.769936 
##           527           528           529           530           531 
##   4614.840619   -243.101973 -10996.865509   4340.936900  13940.929491 
##           532           533           534           535           536 
##  -4742.829538   4518.032476   5752.405097  -1531.022430  -4330.345221 
##           537           538           539           540           541 
##  -6943.194435  -2072.864290   8331.073983    264.469032  -8008.007116 
##           542           543           544           545           546 
##   1831.078894   -547.207717    416.999026 -10968.031567 -11147.389712 
##           547           548           549           550           551 
##   1826.576230   6861.946120  -1327.288480    824.605030  -7705.120494 
##           552           553           554           555           556 
##   8484.982321    972.027237 -11861.106386   9080.770377   8728.794300 
##           557           558           559           560           561 
##    293.814314   5034.940182  -3337.501850  14275.066051  21850.840363 
##           562           563           564           565           566 
##  -5852.737532  -9231.000393   7039.239859    559.905927   3757.004813 
##           567           568           569           570           571 
##  -7056.691624 -17121.563686   6597.336048   6486.727872   2051.913548 
##           572           573           574           575           576 
##   3268.438016   1974.503954  -1949.365917  14887.175661  -9283.252728 
##           577           578           579           580           581 
##  -6051.670230   8803.697490   3073.597833  -6306.478038   7637.164073 
##           582           583           584           585           586 
##  -3570.213359  -2616.781665  15815.010593 -14168.620160   8523.184856 
##           587           588           589           590           591 
##    284.622291  -6012.218652   -653.545504    343.606613 -10555.137624 
##           592           593           594           595           596 
##   1747.811220  -7145.352488   2982.750319   8852.459749  -7372.298245 
##           597           598           599           600           601 
##   5872.216731   2850.665748   7009.440301  -2945.919317   6330.806757 
##           602           603           604           605           606 
##  -8039.937194   2377.414078   1423.176382   3304.426299   1698.891435 
##           607           608           609           610           611 
##    610.443720  -5604.798108   8186.888679   -956.303125  -2377.631133 
##           612           613           614           615           616 
##  -3302.749265  -8129.474665  11945.331754   5112.057680  -9075.462925 
##           617           618           619           620           621 
##  11718.179215   6311.919089  -5243.101375  26583.785051 -12236.334631 
##           622           623           624           625           626 
##  -6433.363802   3378.957628  -3903.778422 -10412.267404  11319.124932 
##           627           628           629           630           631 
## -21435.342055  -2534.949126   8555.349945  11175.684673  -1338.745658 
##           632           633           634           635           636 
##  33465.524242  -5931.367626   6208.213068   5925.150080  -1716.863528 
##           637 
##  -4881.749739 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17350.48 20141.51 24311.61 24035.14 26342.10 23727.35 24429.54 19755.67 
##       10       11       12       13       14       15       16       17 
## 19497.43 16892.58 17654.72 14448.61 14498.76 15150.31 16813.43 15166.58 
##       18       19       20       21       22       23       24       25 
## 16180.85 15566.73 22509.52 21611.59 21101.71 22954.45 22293.72 22933.40 
##       26       27       28       29       30       31       32       33 
## 24742.96 18791.07 20483.19 28165.97 28222.49 27901.85 25578.01 26952.79 
##       34       35       36       37       38       39       40       41 
## 30720.04 31061.60 32442.99 30002.68 34044.90 37189.72 34303.88 31177.65 
##       42       43       44       45       46       47       48       49 
## 30048.14 20813.44 28183.91 30572.34 31640.07 38343.32 37847.87 42418.13 
##       50       51       52       53       54       55       56       57 
## 46570.85 39412.76 34088.33 29209.18 22488.80 28654.77 25302.83 21673.45 
##       58       59       60       61       62       63       64       65 
## 25994.87 27229.58 27520.74 27924.44 23876.79 40140.89 41948.49 37288.20 
##       66       67       68       69       70       71       72       73 
## 41412.18 46230.89 56688.68 54741.62 40275.82 37830.87 40789.26 35201.80 
##       74       75       76       77       78       79       80       81 
## 30743.73 21630.51 24768.85 20774.91 22819.83 17816.03 19791.72 19033.49 
##       82       83       84       85       86       87       88       89 
## 18080.39 16187.41 17439.98 20976.88 25307.48 26277.83 26302.30 26906.87 
##       90       91       92       93       94       95       96       97 
## 30950.60 29804.21 30783.86 28886.47 28102.06 28462.95 28861.51 22565.62 
##       98       99      100      101      102      103      104      105 
## 25521.20 18689.13 17567.82 15647.18 15941.03 16605.05 20989.48 20091.07 
##      106      107      108      109      110      111      112      113 
## 23532.91 23327.03 24970.09 27790.11 25337.58 21851.20 22121.19 24715.27 
##      114      115      116      117      118      119      120      121 
## 35365.49 33593.98 35395.21 38334.39 40251.72 37985.74 32908.91 29322.46 
##      122      123      124      125      126      127      128      129 
## 31364.21 29678.09 30836.34 38286.19 37935.71 37015.76 33941.16 35687.22 
##      130      131      132      133      134      135      136      137 
## 40978.63 40429.96 31805.22 33045.07 36171.91 32653.36 31072.44 30175.49 
##      138      139      140      141      142      143      144      145 
## 26800.44 28186.96 27961.14 25693.08 27677.57 26329.45 20057.79 23028.53 
##      146      147      148      149      150      151      152      153 
## 20898.10 23816.48 24345.82 25904.84 26083.32 27704.85 28979.93 31952.26 
##      154      155      156      157      158      159      160      161 
## 27511.81 26789.31 24392.80 30170.60 41620.02 39983.20 37400.13 42321.36 
##      162      163      164      165      166      167      168      169 
## 43702.13 47050.64 42606.03 38005.80 43324.00 59304.53 61473.01 59918.40 
##      170      171      172      173      174      175      176      177 
## 56806.73 55237.56 57886.81 56930.16 49374.36 52142.16 55810.93 55846.41 
##      178      179      180      181      182      183      184      185 
## 62834.58 53524.28 50339.33 41329.35 33035.64 36474.68 46378.79 45845.54 
##      186      187      188      189      190      191      192      193 
## 51680.84 57312.45 67882.67 73061.28 66880.39 67070.01 74000.80 69762.56 
##      194      195      196      197      198      199      200      201 
## 65373.73 54829.82 48977.76 50373.95 46055.58 38378.92 44676.88 42939.26 
##      202      203      204      205      206      207      208      209 
## 42584.37 43053.28 49713.23 58427.03 58063.95 59754.72 61377.83 65094.20 
##      210      211      212      213      214      215      216      217 
## 74373.92 66612.97 55035.09 49750.29 40924.31 37942.07 40994.28 31211.41 
##      218      219      220      221      222      223      224      225 
## 47849.97 55026.29 55910.39 78226.56 85573.66 87537.35 94978.89 86108.38 
##      226      227      228      229      230      231      232      233 
## 80225.24 79815.43 76531.51 75709.55 80353.08 81690.43 76235.71 71543.12 
##      234      235      236      237      238      239      240      241 
## 77085.19 63996.50 56190.17 48299.66 40076.78 44185.96 46296.21 39876.16 
##      242      243      244      245      246      247      248      249 
## 33680.46 43760.00 38120.43 41978.46 34395.01 33126.41 36709.56 39477.72 
##      250      251      252      253      254      255      256      257 
## 30488.17 36309.04 40035.43 45127.68 47792.35 47294.94 57392.58 74544.45 
##      258      259      260      261      262      263      264      265 
## 74363.15 67805.84 69258.43 65553.81 66971.40 60852.29 50338.59 46444.36 
##      266      267      268      269      270      271      272      273 
## 46649.63 42833.20 51463.57 47810.21 51875.10 50028.85 54015.94 54306.11 
##      274      275      276      277      278      279      280      281 
## 60204.78 57885.71 67367.76 61412.45 61618.34 59999.99 65629.53 59483.75 
##      282      283      284      285      286      287      288      289 
## 56115.42 45873.92 44302.30 61194.46 66615.41 67016.79 64485.35 63590.72 
##      290      291      292      293      294      295      296      297 
## 67512.73 71345.80 52717.37 43014.74 37143.96 47261.67 50439.52 49570.95 
##      298      299      300      301      302      303      304      305 
## 73289.80 79117.88 79772.37 84293.81 82527.93 77656.24 81054.66 56448.78 
##      306      307      308      309      310      311      312      313 
## 52783.67 52469.76 46382.46 43646.96 47179.21 39878.36 38625.67 33294.50 
##      314      315      316      317      318      319      320      321 
## 37004.38 36201.87 39957.75 37981.95 63259.57 61143.32 62734.88 70575.22 
##      322      323      324      325      326      327      328      329 
## 72915.46 97894.46 96304.13 72611.09 71432.66 69822.38 61932.86 59111.85 
##      330      331      332      333      334      335      336      337 
## 29653.00 33267.83 33698.69 35972.87 35326.70 40979.97 42034.36 37375.66 
##      338      339      340      341      342      343      344      345 
## 36605.57 36729.74 32141.02 38021.71 38672.03 38925.38 39783.92 41534.63 
##      346      347      348      349      350      351      352      353 
## 43320.70 43077.25 36161.02 26914.82 32154.11 31037.57 30635.71 28299.14 
##      354      355      356      357      358      359      360      361 
## 32885.78 36566.06 40941.68 39166.38 40401.82 42498.13 49747.60 50287.34 
##      362      363      364      365      366      367      368      369 
## 50485.03 52899.64 50432.67 49888.16 42678.32 39930.35 36182.67 34005.13 
##      370      371      372      373      374      375      376      377 
## 30140.62 37284.89 39526.77 47256.90 41347.34 40805.40 39371.53 38913.52 
##      378      379      380      381      382      383      384      385 
## 29961.16 34468.66 27657.26 35714.62 45844.07 49339.25 47646.65 49599.09 
##      386      387      388      389      390      391      392      393 
## 55696.67 64995.23 58340.30 52917.50 52651.55 59885.73 60398.80 68896.69 
##      394      395      396      397      398      399      400      401 
## 58216.98 59753.00 59321.78 58815.81 57332.51 56118.48 43137.93 51554.47 
##      402      403      404      405      406      407      408      409 
## 50574.58 49561.17 55836.38 48525.74 47850.46 46210.09 41972.46 40826.20 
##      410      411      412      413      414      415      416      417 
## 38928.20 33138.65 40856.41 43725.13 38502.35 33687.31 48266.15 52035.24 
##      418      419      420      421      422      423      424      425 
## 55887.15 48504.57 44900.27 43602.31 47118.17 35765.27 35499.80 29871.61 
##      426      427      428      429      430      431      432      433 
## 35351.41 43515.08 50271.56 47100.68 44227.06 41212.77 41103.16 37650.30 
##      434      435      436      437      438      439      440      441 
## 33864.70 31153.18 32698.25 34511.23 32555.12 37325.51 43408.90 40225.17 
##      442      443      444      445      446      447      448      449 
## 39936.96 42884.21 40811.88 44722.88 40063.33 31272.08 30132.43 41265.86 
##      450      451      452      453      454      455      456      457 
## 40953.67 46493.66 42215.66 42558.44 44147.06 47792.10 37865.61 42619.48 
##      458      459      460      461      462      463      464      465 
## 38132.78 45552.91 49003.69 51572.69 48366.58 50661.31 50858.88 52571.55 
##      466      467      468      469      470      471      472      473 
## 52079.03 54964.88 52345.72 57297.53 50690.06 48347.99 46961.95 43652.71 
##      474      475      476      477      478      479      480      481 
## 47334.94 54651.46 49188.05 50857.78 45749.14 44160.03 46937.24 36559.70 
##      482      483      484      485      486      487      488      489 
## 30247.91 32080.46 34724.09 36184.75 37131.61 30897.50 43181.24 49710.41 
##      490      491      492      493      494      495      496      497 
## 56406.33 51221.84 55959.81 63443.31 67179.74 53706.43 44469.65 42533.48 
##      498      499      500      501      502      503      504      505 
## 42850.33 43625.92 38221.11 40572.85 45770.06 51339.89 52035.60 52143.99 
##      506      507      508      509      510      511      512      513 
## 45969.83 47283.44 43616.25 46317.50 45989.77 39829.18 40938.01 40129.39 
##      514      515      516      517      518      519      520      521 
## 41213.26 43801.34 36782.35 32154.60 55574.22 63574.33 67155.39 60664.75 
##      522      523      524      525      526      527      528      529 
## 61977.88 75296.19 82135.60 57578.77 52549.77 49309.16 53601.96 53118.01 
##      530      531      532      533      534      535      536      537 
## 43494.78 48388.36 60799.69 55428.40 58759.17 62668.45 59779.06 54907.62 
##      538      539      540      541      542      543      544      545 
## 48498.58 47180.93 54961.82 54717.15 47423.64 49603.49 49433.57 50113.75 
##      546      547      548      549      550      551      552      553 
## 40946.82 32943.28 37199.63 45156.43 44957.39 46629.69 40757.45 49592.97 
##      554      555      556      557      558      559      560      561 
## 50725.53 40705.94 50059.06 57767.04 57144.49 60671.36 56521.93 68050.87 
##      562      563      564      565      566      567      568      569 
## 84410.88 74697.00 63485.76 67817.95 65979.28 67142.55 58878.56 43182.95 
##      570      571      572      573      574      575      576      577 
## 50053.56 55842.37 57001.85 59036.50 59670.79 56853.82 68859.25 58441.96 
##      578      579      580      581      582      583      584      585 
## 52288.59 59740.40 61214.76 54444.84 60587.93 56251.21 53353.99 66656.76 
##      586      587      588      589      590      591      592      593 
## 52372.39 59571.95 58682.22 52528.12 51846.96 52117.57 43016.33 45758.07 
##      594      595      596      597      598      599      600      601 
## 40490.39 44652.54 53243.16 46705.78 52449.33 54780.27 60337.63 56571.48 
##      602      603      604      605      606      607      608      609 
## 61290.37 53025.16 54868.11 55629.15 57891.82 58454.56 58004.37 52296.54 
##      610      611      612      613      614      615      616      617 
## 59219.02 57317.35 54471.75 51242.76 44344.38 55627.80 59438.61 50552.68 
##      618      619      620      621      622      623      624      625 
## 60749.65 64852.10 58470.21 80259.62 65675.65 58156.19 60119.64 55564.55 
##      626      627      628      629      630      631      632      633 
## 46090.45 56586.77 37526.38 37389.36 46769.03 57045.03 55128.19 83290.80 
##      634      635      636      637 
## 73670.50 75827.85 77432.86 72263.18 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8258
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    8.067624  0.5253621    3.554446
## t2* 1719.724030 21.3552523  207.455958
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    3.487917       8.208575   15.04538
## 2    lag_depvar 1421.720200    1730.303658 2096.58555

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Nov 06 01:04:43 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Nov 06 01:04:52 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Nov 06 01:05:01 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Nov 06 01:05:10 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Nov 06 01:05:19 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Nov 06 01:05:28 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Nov 06 01:05:37 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Nov 06 01:05:47 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Nov 06 01:05:56 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Nov 06 01:06:05 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 4.7264 5.410333 5.629750 6.5571957
Comida 366.5578 310.278417 314.087500 346.0626522
Comunicaciones 0.0000 0.000000 0.000000 0.0000000
Electricidad 31.9587 47.072333 38.297667 32.3040435
Enceres 20.2717 20.086417 17.443792 23.6850435
Farmacia 1.9980 1.831667 7.913875 8.2250870
Gas/Bencina 34.4632 44.325000 28.954333 27.1007826
Diosi 38.1981 31.180667 41.934250 39.8665000
donaciones/regalos 0.0000 0.000000 7.170083 5.9721522
Electrodomésticos/ Mantención casa 0.0000 3.944000 30.269500 18.0319130
VTR 13.1950 25.156667 22.121792 19.3964783
Netflix 4.6340 7.151583 7.090167 6.8580652
Otros 0.0000 3.151083 1.575542 0.8220217
Total 516.0029 499.588167 522.488250 534.8819348
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2139, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-11-09 00:04:58 sería de: 37.238 pesos// Percentil 95% más alto proyectado: 40.466,95

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36505.90 36499.36
Lo.80 36554.68 36555.03
Point.Forecast 37238.50 38908.42
Hi.80 39046.10 43642.85
Hi.95 40038.24 46149.10


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2744  1010.8814
## s.e.  0.1326    29.4442
## 
## sigma^2 = 27152:  log likelihood = -370.86
## AIC=747.73   AICc=748.18   BIC=753.86
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,1) errors 
## 
## Coefficients:
##          ar1      ma1     xreg
##       0.8729  -0.6504  32.1604
## s.e.  0.1232   0.1922   1.7310
## 
## sigma^2 = 25266:  log likelihood = -368.4
## AIC=744.81   AICc=745.57   BIC=752.98
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 933.1271 675.0316 750.6628
Lo.80 1051.1308 791.2810 837.7665
Point.Forecast 1274.0451 1010.8814 1030.8347
Hi.80 1496.9594 1230.4817 1268.3966
Hi.95 1614.9631 1346.7312 1415.5759


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 66 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.7      
##  [7] tidytext_0.4.1      DT_0.30             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.3       xts_0.13.1         
## [13] forecast_8.21.1     wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.1     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.3     forcats_1.0.0      
## [22] dplyr_1.1.3         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.2.1        ggplot2_3.4.4       tidyverse_2.0.0    
## [28] sjPlot_2.8.15       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-3       sparklyr_1.8.4      httr_1.4.7         
## [34] readxl_1.4.3        zoo_1.8-12          stringr_1.5.0      
## [37] stringi_1.7.12      data.table_1.14.8   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.9          readr_2.1.4        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.5    
##  [10] fansi_1.0.4         ggfortify_0.4.16    magrittr_2.0.3     
##  [13] tzdb_0.4.0          modelr_0.1.11       vroom_1.6.4        
##  [16] askpass_1.1         timechange_0.2.0    anytime_0.3.9      
##  [19] tseries_0.10-54     colorspace_2.1-0    xfun_0.39          
##  [22] crayon_1.5.2        jsonlite_1.8.4      lme4_1.1-35.1      
##  [25] glue_1.6.2          gtable_0.3.4        emmeans_1.8.9      
##  [28] sjstats_0.18.2      sjmisc_2.8.9        car_3.1-2          
##  [31] quantmod_0.4.25     abind_1.4-5         mvtnorm_1.2-3      
##  [34] DBI_1.1.3           ggeffects_1.3.2     Rcpp_1.0.10        
##  [37] viridisLite_0.4.2   xtable_1.8-4        performance_0.10.8 
##  [40] bit_4.0.5           htmlwidgets_1.6.2   timeSeries_4031.107
##  [43] gplots_3.1.3        ellipsis_0.3.2      spatial_7.3-14     
##  [46] pkgconfig_2.0.3     farver_2.1.1        nnet_7.3-16        
##  [49] sass_0.4.5          dbplyr_2.4.0        janitor_2.2.0      
##  [52] utf8_1.2.3          tidyselect_1.2.0    labeling_0.4.3     
##  [55] rlang_1.1.2         munsell_0.5.0       cellranger_1.1.0   
##  [58] tools_4.1.2         cachem_1.0.7        cli_3.6.1          
##  [61] generics_0.1.3      sjlabelled_1.2.0    broom_1.0.5        
##  [64] evaluate_0.20       fastmap_1.1.1       yaml_2.3.7         
##  [67] knitr_1.45          bit64_4.0.5         caTools_1.18.2     
##  [70] nlme_3.1-153        slam_0.1-50         xml2_1.3.3         
##  [73] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.14    
##  [76] curl_5.1.0          bslib_0.4.2         highr_0.10         
##  [79] fBasics_4032.96     Matrix_1.6-1.1      its.analysis_1.6.0 
##  [82] nloptr_2.0.3        urca_1.3-3          vctrs_0.6.4        
##  [85] pillar_1.9.0        lifecycle_1.0.3     lmtest_0.9-40      
##  [88] jquerylib_0.1.4     estimability_1.4.1  bitops_1.0-7       
##  [91] insight_0.19.6      R6_2.5.1            KernSmooth_2.23-20 
##  [94] janeaustenr_1.0.0   codetools_0.2-18    assertthat_0.2.1   
##  [97] boot_1.3-28         MASS_7.3-54         gtools_3.9.4       
## [100] openssl_2.0.6       withr_2.5.2         fracdiff_1.5-2     
## [103] bayestestR_0.13.1   parallel_4.1.2      hms_1.1.3          
## [106] quadprog_1.5-8      timeDate_4022.108   minqa_1.2.6        
## [109] snakecase_0.11.1    rmarkdown_2.25      carData_3.0-5      
## [112] TTR_0.24.3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))